Track accepted paper

CiteScore:
3.49ℹCiteScore:2018: 3.490CiteScore measures the average citations received per document published in this title. CiteScore values are based on citation counts in a given year (e.g. 2015) to documents published in three previous calendar years (e.g. 2012 – 14), divided by the number of documents in these three previous years (e.g. 2012 – 14).

Impact Factor:
2.259ℹImpact Factor:2018: 2.259The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years.
2018 Journal Citation Reports (Clarivate Analytics, 2019)

5-Year Impact Factor:
2.591ℹFive-Year Impact Factor:2018: 2.591To calculate the five year Impact Factor, citations are counted in 2018 to the previous five years and divided by the source items published in the previous five years.
2018 Journal Citation Reports (Clarivate Analytics, 2019)

Source Normalized Impact per Paper (SNIP):
1.567ℹSource Normalized Impact per Paper (SNIP):2018: 1.567SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.

SCImago Journal Rank (SJR):
0.508ℹSCImago Journal Rank (SJR):2018: 0.508SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.

Author StatsℹAuthor Stats:Publishing your article with us has many benefits, such as having access to a personal dashboard: citation and usage data on your publications in one place. This free service is available to anyone who has published and whose publication is in Scopus.

Special Issue: Integrating Vision and Language for Semantic Knowledge Reasoning and Transfer

Aims and Scope:

Due to the explosive growth of visual and textual data (e.g., images, video, blogs) on the Internet and the urgent requirement of joint understanding the heterogeneous data, integrating vision and language to bridge the semantic gap has attracted a huge amount of interest from the computer vision and natural language processing communities. Great efforts have been made to study the intersection of vision and language, and fantastic applications include (i) generating image descriptions using natural language, (ii) visual question answering, (iii) retrieval of images based on textural queries (and vice versa), (iv) generating images/videos from textual descriptions, (v) language grounding and many other related topics.

Though booming recently, it remains challenging as reasoning of the connections between visual contents and linguistic words are difficult. Reasoning is based on semantic knowledge, i.e. people understanding a linguistic word (for example “swan”) involves reasoning the external knowledge of the word (e.g., what swan look like, the sounds they make, how they behave and what their skin feels like.) Although reasoning ability is always claimed in recent studies, most “reasoning” simply uncovers latent connections between visual elements and textual/semantic facts during the training on manually annotated datasets with a large number of image-text pairs. Furthermore, recent studies are always specific to certain datasets that lack generalization ability, i.e., the semantic knowledge obtained from specific dataset cannot be directly transferred to other datasets, as different benchmark may have different characteristics of its own. One potential solution is leveraging external knowledge resources (e.g., social-media sites, expert systems and Wikipedia) as intermediate bridge for knowledge transfer. However, it is still implicit that how to appropriately incorporate the comprehensive knowledge resources for more effective knowledge-based reasoning and transfer across datasets. Towards a broad perspective of applications, integrating vision and language for knowledge reasoning and transfer has yet been well exploited in existing research.

Topics of Interests:

This special issue targets the researchers and practitioners from both the academia and industry to explore how advanced learning models and systems can be leveraged to address the challenges in semantic knowledge reasoning and transfer for joint understanding vision and language. It provides a forum to publish recent state-of-the-art research findings, methodologies, technologies and services in vision-language technology for practical applications. We invite original and high quality submissions addressing all aspects of this field, which is closely related to multimedia search, multi-modal learning, cross-media analysis, cross-knowledge transfer and so on.

Topics of interest include, but are not limited to:

· Big data storage, indexing, and searching

· Deep learning methods for vision and language

· Transfer learning for vision and language

· Cross-media analysis (retrieval, hashing, transfer, reasoning, etc)

· Multi-modal learning and semantic representation learning

· Learning knowledge graph over multi-modal data

· Generating image/video descriptions using natural language

· Visual question answering/generation

· Retrieval of images based on textural queries (and vice versa)

· Generating images/videos from textual descriptions

· Language grounding

Submission Guideline:

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals. Authors should prepare their manuscripts according to the “Instructions for Authors” of “Journal of Visual Communication and Image Representation” guidelines at the journal website https://www.journals.elsevier.com/journal-of-visual-communication-and-image-representation. When submitting via this page, please select “VSI: Vis-Lang” as the Article Type.

All papers will be peer-reviewed following a regular reviewing procedure. Each submission should clearly demonstrate evidence of benefits to society or large communities. Originality and impact on research scopes, in combination with a media-related focus and innovative technical aspects of the proposed solutions will be the major evaluation criteria.